Raman spectroscopy has found applications in a wide variety of areas that involve identifying and characterizing chemicals. For example, Raman spectroscopy has been used in microelectronics fabrication, criminal investigations, biochemistry, archaeology, biomedical areas, military applications, and homeland security.
Infrared (IR) spectroscopy is another technique for characterizing chemicals and chemical processes; however, Raman spectroscopy has several advantages over IR spectroscopy. Firstly, not all chemicals are IR active. Due to the complementary nature of these two methods, such IR-inactive chemicals become visible to Raman spectrometers. Secondly, aqueous solutions render IR spectrometers ineffective, whereas they are inconsequential to Raman spectrometers. Other issues such as, for example, sampling or interference from glass backgrounds plague IR spectroscopy. Raman spectroscopy, therefore, is attractive for such IR-unfriendly scenarios.
The advantages of Raman spectroscopy over IR spectroscopy come at a cost of a low signal-to-interference-and-noise ratio (SINR). Signal degradation sources assume greater importance leading to formidable signal processing challenges for achieving adequate interference and noise reduction, and signal detection and discrimination performance. Two major sources contributing to “background” interferences are fluorescence of the environment and/or the target chemical itself, and the light collecting instrument's transfer function (IRC). The effect of these two sources of background are collectively termed the “pedestal,” as they non-uniformly raise and reshape the spectral plot of a given ideal background-free chemical spectrum. There is a need for an algorithm that is capable of detecting and identifying chemical Raman signatures in Raman shift data while accounting for the pedestal interferences.